README.md

metadata

base_model: microsoft/dit-base
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: dit-base-Classifier_CM05_V2
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0

dit-base-Classifier_CM05_V2

This model is a fine-tuned version of microsoft/dit-base on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.8224
• Accuracy: 0.0
• Weighted f1: 0.0
• Micro f1: 0.0
• Macro f1: 0.0
• Weighted recall: 0.0
• Micro recall: 0.0
• Macro recall: 0.0
• Weighted precision: 0.0
• Micro precision: 0.0
• Macro precision: 0.0

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 18

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Weighted f1	Micro f1	Macro f1	Weighted recall	Micro recall	Macro recall	Weighted precision	Micro precision	Macro precision
0.236	1.0	1	0.3301	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.236	2.0	2	0.5344	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.236	3.0	3	1.4354	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.236	4.0	4	2.9869	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.236	5.0	5	3.6242	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.236	6.0	6	3.4178	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.236	7.0	7	2.9061	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1227	8.0	8	2.3784	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1227	9.0	9	1.9251	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1227	10.0	10	1.5871	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1227	11.0	11	1.3348	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1227	12.0	12	1.1845	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1227	13.0	13	1.0686	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1227	14.0	14	0.9755	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1227	15.0	15	0.9099	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0376	16.0	16	0.8643	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0376	17.0	17	0.8357	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0376	18.0	18	0.8224	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.1+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1